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  • × author_ss:"Schreiber, M."
  • × year_i:[2000 TO 2010}
  1. Schreiber, M.: Revisiting the g-index : the average number of citations in the g-core (2009) 0.00 
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    Abstract
    The g-index is discussed in terms of the average number of citations of the publications in the g-core, showing that it combines features of the h-index and the A-index in one number. For a visualization, data of 8 famous physicists are presented and analyzed. In comparison with the h-index, the g-index increases between 67% and 144%, on average by a factor of 2.
    Type
    a
  2. Schreiber, M.: ¬An empirical investigation of the g-index for 26 physicists in comparison with the h-index, the A-index, and the R-index (2008) 0.00 
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    Abstract
    J.E. Hirsch (2005) introduced the h-index to quantify an individual's scientific research output by the largest number h of a scientist's papers that received at least h citations. To take into account the highly skewed frequency distribution of citations, L. Egghe (2006a) proposed the g-index as an improvement of the h-index. I have worked out 26 practical cases of physicists from the Institute of Physics at Chemnitz University of Technology, and compare the h and g values in this study. It is demonstrated that the g-index discriminates better between different citation patterns. This also can be achieved by evaluating B.H. Jin's (2006) A-index, which reflects the average number of citations in the h-core, and interpreting it in conjunction with the h-index. h and A can be combined into the R-index to measure the h-core's citation intensity. I also have determined the A and R values for the 26 datasets. For a better comparison, I utilize interpolated indices. The correlations between the various indices as well as with the total number of papers and the highest citation counts are discussed. The largest Pearson correlation coefficient is found between g and R. Although the correlation between g and h is relatively strong, the arrangement of the datasets is significantly different depending on whether they are put into order according to the values of either h or g.
    Object
    A-Index
    Type
    a
  3. Schreiber, M.: ¬A case study of the modified Hirsch index hm accounting for multiple coauthors (2009) 0.00 
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    Abstract
    J.E. Hirsch (2005) introduced the h-index to quantify an individual's scientific research output by the largest number h of a scientist's papers, that received at least h citations. This so-called Hirsch index can be easily modified to take multiple coauthorship into account by counting the papers fractionally according to (the inverse of) the number of authors. I have worked out 26 empirical cases of physicists to illustrate the effect of this modification. Although the correlation between the original and the modified Hirsch index is relatively strong, the arrangement of the datasets is significantly different depending on whether they are put into order according to the values of either the original or the modified index.
    Type
    a
  4. Schreiber, M.: Fractionalized counting of publications for the g-Index (2009) 0.00 
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    Abstract
    L. Egghe ([2008]) studied the h-index (Hirsch index) and the g-index, counting the authorship of cited articles in a fractional way. But his definition of the gF-index for the case that the article count is fractionalized yielded values that were close to or even larger than the original g-index. Here I propose an alternative definition by which the g-index is modified in such a way that the resulting gm-index is always smaller than the original g-index. Based on the interpretation of the g-index as the highest number of articles of a scientist that received on average g or more citations, in the specification of the new gm-index the articles are counted fractionally not only for the rank but also for the average.
    Type
    a
  5. Schreiber, M.: Das Web ist eine Wolke (2009) 0.00 
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    Content
    Ein Grid dient hauptsächlich zur Bewältigung rechenintensiver Aufgaben. Der Unterschied zu Clustern: Grids bestehen aus einer losen Verkettung weltweit verstreuter Server, denen sich diverse Institutionen anschließen können. Standardisierte Bibliotheken und Middleware erleichtern die Zusammenarbeit: Die dritte Voraussetzung für das Cloud Computing ist das Utility Computing. Hier bieten Unternehmen Leistungen wie Onlinespeicher, virtuelle Server und Software als gebündelten Service an und rechnen nach verbrauchter Leistung ab. Neue Möglichkeiten: Software nach dem Baukastenprinzip Auf diesen Grundlagen aufbauend entsteht das Cloud Computing. Es verbindet die Komponenten und eröffnet dadurch diverse Möglichkeiten, etwa die "Infrastructure as a Service" (IaaS): Die Betreiber übernehmen die komplette Infrastruktur, etwa virtualisierte Hardware. Diese ist wie bei der"Amazon Elastic Compute Cloud" (EC2) je nach Anforderung skalierbar. Die "Platform as a Service" (PaaS) richtet sich hauptsächlich an Entwickler: Hier stellt der Betreiber kein Enduser-Programm, sondern eine komplette Arbeitsumgebung bereit. So können Software-Anbieter eigene Webapplikationen schreiben und vertreiben. Das wohl bekannteste Beispiel ist die "Google App Engine' die Python als Programmiersprache sowie das Python-Web-Framework "Django" einsetzt. Die fertige Software liegt auf den Servern des Betreibers und benötigt weder eine lokale Installation noch eigene Hardware. PaaS wird daher auch als "Cloudware" bezeichnet.
    Der dritte Ansatz des Cloud Computings ist die "Software as a Service" (SaaS): Im Gegensatz zum klassischen Modell, bei dem der Kunde eine Software kauft und sie auf seinem PC installiert, kann der Nutzer die Programme beim SaaS nur "mieten". Die Tools laufen im Browser und sind in der Regel plattformunabhängig. Während der User die Angebote lediglich nutzt, bietet die Verbindung aller Teilbereiche vor allem jungen Start-ups, wesentliche Vorteile: Da sie nicht mehr auf eigene Server angewiesen sind, sinkt der Kostendruck. So ist es möglich, eine Webseite ohne eigene Hardware aufzubauen und bei Bedarf mehr Rechenpower zu mieten. Zehn.de (www.zehn.de) etwa, ist das erste deutsche Portal, das die vernetzten Strukturen des Cloud Computings voll ausschöpft. Während die Entwickler für die gesamte Kommunikation der Seite wie Front- und Backend, Datensätze und -filter auf die Google App Engine setzen, liegt die Software zur semantischen Analyse der Inhalte bei Amazon EC2 und Bilder sowie Videos bei Amazon S3."
    Type
    a

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